HealthFlow: A Self-Evolving AI Agent with Meta Planning for Autonomous Healthcare Research
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908586892853248 |
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| author | Zhu, Yinghao Qi, Yifan Wang, Zixiang Gu, Lei Sui, Dehao Hu, Haoran Zhang, Xichen He, Ziyi He, Junjun Ma, Liantao Yu, Lequan |
| author_facet | Zhu, Yinghao Qi, Yifan Wang, Zixiang Gu, Lei Sui, Dehao Hu, Haoran Zhang, Xichen He, Ziyi He, Junjun Ma, Liantao Yu, Lequan |
| contents | The rapid proliferation of scientific knowledge presents a grand challenge: transforming this vast repository of information into an active engine for discovery, especially in high-stakes domains like healthcare. Current AI agents, however, are constrained by static, predefined strategies, limiting their ability to navigate the complex, evolving ecosystem of scientific research. This paper introduces HealthFlow, a self-evolving AI agent that overcomes this limitation through a novel meta-level evolution mechanism. HealthFlow autonomously refines its high-level problem-solving policies by distilling procedural successes and failures into a durable, structured knowledge base, enabling it to learn not just how to use tools, but how to strategize. To anchor our research and provide a community resource, we introduce EHRFlowBench, a new benchmark featuring complex health data analysis tasks systematically derived from peer-reviewed scientific literature. Our experiments demonstrate that HealthFlow's self-evolving approach significantly outperforms state-of-the-art agent frameworks. This work offers a new paradigm for intelligent systems that can learn to operationalize the procedural knowledge embedded in scientific content, marking a critical step toward more autonomous and effective AI for healthcare scientific discovery. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_02621 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HealthFlow: A Self-Evolving AI Agent with Meta Planning for Autonomous Healthcare Research Zhu, Yinghao Qi, Yifan Wang, Zixiang Gu, Lei Sui, Dehao Hu, Haoran Zhang, Xichen He, Ziyi He, Junjun Ma, Liantao Yu, Lequan Artificial Intelligence Computation and Language Machine Learning Multiagent Systems The rapid proliferation of scientific knowledge presents a grand challenge: transforming this vast repository of information into an active engine for discovery, especially in high-stakes domains like healthcare. Current AI agents, however, are constrained by static, predefined strategies, limiting their ability to navigate the complex, evolving ecosystem of scientific research. This paper introduces HealthFlow, a self-evolving AI agent that overcomes this limitation through a novel meta-level evolution mechanism. HealthFlow autonomously refines its high-level problem-solving policies by distilling procedural successes and failures into a durable, structured knowledge base, enabling it to learn not just how to use tools, but how to strategize. To anchor our research and provide a community resource, we introduce EHRFlowBench, a new benchmark featuring complex health data analysis tasks systematically derived from peer-reviewed scientific literature. Our experiments demonstrate that HealthFlow's self-evolving approach significantly outperforms state-of-the-art agent frameworks. This work offers a new paradigm for intelligent systems that can learn to operationalize the procedural knowledge embedded in scientific content, marking a critical step toward more autonomous and effective AI for healthcare scientific discovery. |
| title | HealthFlow: A Self-Evolving AI Agent with Meta Planning for Autonomous Healthcare Research |
| topic | Artificial Intelligence Computation and Language Machine Learning Multiagent Systems |
| url | https://arxiv.org/abs/2508.02621 |